diff --git a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md
index 615f8307..7b3bba4f 100644
--- a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md
+++ b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md
@@ -92,9 +92,9 @@ Supported models: Llama2-7B, Llama3-8B, Qwen2-1.5B, Qwen2-7B, MiniCPM-1B, MiniCP
 
 ### Recommended NPU Driver Version for LNL Users
 #### 32.0.100.2625
-Supported models: Llama2-7B, Qwen2-1.5B, Qwen2-7B, MiniCPM-1B, Baichuan2-7B
+Supported models: Llama2-7B, MiniCPM-1B, Baichuan2-7B
 #### 32.0.101.2715
-Supported models: Llama3-8B, MiniCPM-2B
+Supported models: Llama3-8B, MiniCPM-2B, Qwen2-7B, Qwen2-1.5B
 
 ### Run
 ```bash
diff --git a/python/llm/src/ipex_llm/transformers/npu_models/qwen2_mp.py b/python/llm/src/ipex_llm/transformers/npu_models/qwen2_mp.py
index 9ddad939..dc896236 100644
--- a/python/llm/src/ipex_llm/transformers/npu_models/qwen2_mp.py
+++ b/python/llm/src/ipex_llm/transformers/npu_models/qwen2_mp.py
@@ -634,7 +634,7 @@ def run_decode(
     with torch.inference_mode():
         while True:
 
-            dist.broadcast(control, src=0)
+            dist.broadcast(control, src=0, async_op=False)
             if control.item() == -2:
                 break
             elif control.item() == -1:
@@ -708,6 +708,8 @@ class DecodeRunner:
         self.output_queues = []
         self.decoder_processes = []
 
+        self.forward_signal = torch.tensor(0, dtype=torch.int)
+
         for rank in range(1, world_size):
             input_q = mp.Queue()
             output_q = mp.Queue()
@@ -763,13 +765,14 @@ class DecodeRunner:
             for i in range(len(self.decoder_processes)):
                 self.input_queues[i].put(past_key_value)
 
-        control = torch.tensor(0, dtype=torch.int)
-        dist.broadcast(control, src=0)
+        t0 = time.perf_counter()
+        dist.broadcast(self.forward_signal, src=0, async_op=True)
+        t1 = time.perf_counter()
         hidden_states = hidden_states.to(torch.float16)
         dist.send(hidden_states, dst=1)
         past_key_value.expand(self.transpose_value_cache)
         dist.recv(hidden_states, src=self.world_size - 1)
-        t1 = time.perf_counter()
+        t2 = time.perf_counter()
         return hidden_states, past_key_value
 
     def shutdown(self):